Hostname: page-component-77c89778f8-n9wrp Total loading time: 0 Render date: 2024-07-20T11:22:58.687Z Has data issue: false hasContentIssue false
  • English
  • Français

Proximal sensing for monitoring the productivity of a permanent Mediterranean pasture: influence of rainfall patterns

Published online by Cambridge University Press:  01 June 2017

J. Serrano ,
S. Shahidian ,
J. Marques da Silva ,
F. Moral  and
F. Rebollo
J. Serrano*
Affiliation:
Departamento de Engenharia Rural, Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM), Escola de Ciências e Tecnologia, Universidade de Évora, Apartado 94, 7002-554 Évora, Portugal
S. Shahidian
Affiliation:
Departamento de Engenharia Rural, Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM), Escola de Ciências e Tecnologia, Universidade de Évora, Apartado 94, 7002-554 Évora, Portugal
J. Marques da Silva
Affiliation:
Departamento de Engenharia Rural, Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM), Escola de Ciências e Tecnologia, Universidade de Évora, Apartado 94, 7002-554 Évora, Portugal
F. Moral
Affiliation:
Departamento de Expresión Gráfica, Universidad de Extremadura, Badajoz, Spain
F. Rebollo
Affiliation:
Departamento de Expresión Gráfica, Universidad de Extremadura, Badajoz, Spain
*
†E-mail: jmrs@uevora.pt
Get access

Abstract

The main objective of this work was to evaluate technologies that have potential for monitoring aspects related to spatial and temporal variability of soil nutrients and pasture yield and for support to decision making by the farmers. Three types of sensors were evaluated: an electromagnetic induction sensor, an active optical sensor and a capacitance probe. The results are relevant for the selection of the adequate sensing system for each particular application and to open new perspectives for other works that would allow the testing, calibration and validation of the sensors in a wider range of pasture production conditions and rainfall patterns, characteristic of the Mediterranean region.

Keywords

sensorspastures productivitysoilspatial and temporal patterns
Type
Precision Pasture
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 796 - 801
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Corwin, DL and Lesch, SM 2005. Apparent soil electrical conductivity measurements in agriculture. Computer and Electronic in Agriculture 46 (1–3), 11–43.CrossRefGoogle Scholar
Dusseux, P, Moy-Hubert, L, Corpetti, T and Vertès, F 2015. Evaluation of SPOT imagery for the estimation of grassland biomass. International Journal of Applied Earth Observation and Geoinformation 38, 72–77.CrossRefGoogle Scholar
Efe Serrano, J 2006. Pastures in Alentejo: Technical basis for characterization, grazing and improvement. Universidade de Évora, ICAM, Évora, Portugal, pp 165–178.Google Scholar
FAO 2006. World reference base for soil resources. World Soil Resources Reports No 103. Food and Agriculture Organization of the United Nations, Rome, Italy.Google Scholar
Gitelson, AA 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology 161 (2), 165–173.CrossRefGoogle ScholarPubMed
Mallarino, AP and Wittry, DJ 2004. Efficacy of grid and zone soil sampling approaches for site-specific assessment of phosphorus, potassium, pH, and organic matter. Precision Agriculture 5, 131–144.CrossRefGoogle Scholar
Murray, RI, Yule, IJ and Gillingham, AG 2007. Developing variable rate application technology: Modelling annual pasture production on hill country. New Zealand Journal of Agricultural Research 50 (1), 41–52.Google Scholar
Schaefer, MT and Lamb, DW 2016. A combination of plant NDVI and LiDAR measurements improve the estimation of pasture biomass in Tall Fescue (Festuca arundinacea var. Fletcher). Remote Sensing 8 (2), 1–10.CrossRefGoogle Scholar
Schellberg, J, Hill, MJ, Gerhards, R, Rothmund, M and Braun, M 2008. Precision agriculture on grassland: Applications, perspectives and constraints. European Journal of Agronomy 29 (2–3), 59–71.CrossRefGoogle Scholar
Serrano, J, Peça, J, Marques da Silva, J and Shahidian, S 2010. Mapping soil and pasture variability with an electromagnetic induction sensor. Computer and Electronic in Agriculture 73, 7–16.CrossRefGoogle Scholar
Serrano, J, Peça, J, Marques da Silva, J and Shahidian, S 2011. Calibration of a capacitance probe for measurement and mapping of dry matter yield in Mediterranean pastures. Precision Agriculture 12, 860–875.CrossRefGoogle Scholar
Serrano, J, Shahidian, S and Marques da Silva, J 2016a. Monitoring pasture variability: optical OptRx® crop sensor versus Grassmaster II capacitance probe. Environmental Monitoring and Assessment 188 (117), 1–17.CrossRefGoogle ScholarPubMed
Serrano, J, Shahidian, S and Marques da Silva, J 2016b. Calibration of Grassmaster II to estimate green and dry matter yield in Mediterranean pastures: effects of pasture moisture content. Crop & Pasture Science 67, 780–791.CrossRefGoogle Scholar